--- library_name: "ofoldx" tags: - "biology" - "biomolecular-design" - "protein" - "rna" - "dna" - "pipeline" - "proteinmpnn" - "design-generation" - "protein-design" artifact_kind: "pipeline" repo_id: "oteam/solublempnn-noise020" license: "mit" pipeline_tag: "other" task: "design_generation" model-index: - name: "solublempnn-noise020" results: [] widget: - pipeline_tag: "other" task: "design_generation" example_title: "Backbone sequence design" text: "input_structure: backbone.cif\ndesign_chains: A" input_format: "structure_path" - pipeline_tag: "other" task: "design_generation" example_title: "Binder design" text: "target_structure: target.cif\ntarget_chains: A\ndesign_chains: B" input_format: "structure_path" --- # solublempnn-noise020 OFoldX `pipeline` artifact for biomolecular design generation, using the `proteinmpnn` architecture. ## Disclaimer This model card was generated by the OFoldX team for an OFoldX `pipeline` artifact. The upstream model authors did not write this card unless explicitly stated otherwise. OFoldX is pre-alpha research software. Check the source checkpoint, upstream release, and local validation before using the artifact for scientific or operational decisions. ## Model Details ProteinMPNN sequence-design model for protein backbones, including soluble and membrane variants. Converted SolubleMPNN sequence-design checkpoint for soluble protein backbones. ### Model Provenance - **Upstream Project**: SolubleMPNN - **Source Release**: [https://github.com/dauparas/LigandMPNN](https://github.com/dauparas/LigandMPNN) - **Primary Paper**: [Computational design of soluble and functional membrane protein analogues](https://doi.org/10.1038/s41586-024-07601-y) - **Upstream License**: MIT for upstream ProteinMPNN and LigandMPNN code/model parameters ### Model Specification | Field | Value | | ----- | ----- | | Repository | `oteam/solublempnn-noise020` | | Artifact Kind | `pipeline` | | Task | `design_generation` | | Architecture | `proteinmpnn` | | Entrypoint | `ofoldx.pipelines.design.DesignPipeline` | > [!NOTE] > Checkpoint metadata: `k_neighbors=48`; the `noiseXXX` suffix identifies the training-noise variant. ### Links - **Hub repository**: [oteam/solublempnn-noise020](https://huggingface.co/oteam/solublempnn-noise020) - **Upstream paper**: [Computational design of soluble and functional membrane protein analogues](https://doi.org/10.1038/s41586-024-07601-y) - **Upstream repository**: [SolubleMPNN](https://github.com/dauparas/ProteinMPNN/tree/main/soluble_model_weights) - **Source checkpoint release**: [https://github.com/dauparas/LigandMPNN](https://github.com/dauparas/LigandMPNN) - **Code**: [`ofoldx/pipelines/design.py`](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/pipelines/design.py) - **Project repository**: [https://github.com/OTeam-AI4S/OFoldX](https://github.com/OTeam-AI4S/OFoldX) - **Issues**: [https://github.com/OTeam-AI4S/OFoldX/issues](https://github.com/OTeam-AI4S/OFoldX/issues) ## Usage The artifact depends on the [`ofoldx`](https://github.com/OTeam-AI4S/OFoldX) library. Install it with pip: ```bash pip install ofoldx ``` ### Pipeline Usage Load the artifact from `oteam/solublempnn-noise020` with the OFoldX task pipeline. Use `AutoModel` or `AutoProcessor` only when you need lower-level control: ```python from ofoldx.pipelines import Pipeline pipeline = Pipeline.from_pretrained("oteam/solublempnn-noise020") ``` When a matching processor is available, load it with `AutoProcessor.from_pretrained(...)` and pass the processed batch to the model. ### Interface - **Task**: `design_generation` - **Artifact kind**: `pipeline` - **Architecture**: `proteinmpnn` - **Runtime files**: `manifest.json`, `config.json`, and `model.safetensors` when present ## Training Details OFoldX did not train these weights. This repository contains a converted checkpoint and OFoldX runtime metadata for loading it. ### Training Data The SolubleMPNN work trains like ProteinMPNN on PDB assemblies as of 2021-08-02, filtered to X-ray/cryo-EM structures better than 3.5 A and fewer than 10,000 residues, while excluding annotated transmembrane PDB entries. OFoldX does not redistribute the training set. ### Training Procedure Upstream SolubleMPNN follows ProteinMPNN-style fixed-backbone inverse-folding training with 48-neighbor noisy-backbone checkpoint variants. OFoldX converts released SolubleMPNN checkpoints into `model.safetensors`; it does not run SolubleMPNN training. ## Evaluation OFoldX conversion reports and contract tests validate artifact structure and checkpoint loading. Task-level scientific evaluation should be checked against the corresponding upstream model release or paper. ## Limitations - This artifact is distributed for research use. - Inputs must match the model-specific processor and expected biomolecular representation. - OFoldX is pre-alpha, so APIs and artifact metadata may still change before a stable release. ## Citation Please cite the upstream SolubleMPNN work for the source checkpoint. If OFoldX supports your work, please also cite or link the OFoldX project repository. ```bibtex @article{solublempnn2024membrane, title = {Computational design of soluble and functional membrane protein analogues}, journal = {Nature}, year = {2024}, doi = {10.1038/s41586-024-07601-y} } ``` ## Contact Please use [OFoldX GitHub issues](https://github.com/OTeam-AI4S/OFoldX/issues) for questions or comments about this model card. ## License The Hub `license` metadata, when present, reflects the source checkpoint or upstream project license. The OFoldX project license is not yet finalized. The source checkpoint is associated with the upstream license noted above: MIT for upstream ProteinMPNN and LigandMPNN code/model parameters. Review both OFoldX and upstream terms before redistribution or production use.
